Week 4) Engineering data sets

Learning Goals

• Apply data wrangling techniques using the Tidyverse to clean, transform, and prepare datasets for analysis.
• Structure R scripts into modular components (setup, input, transformation, output) to facilitate reproducibility and automation.
• Implement common data operations (e.g., merging, aggregating, reshaping) and integrate basic programming concepts in R.
• Develop new variables and features (feature engineering) to enhance the analysis and understanding of datasets.

Preparation before class

• Please read chapter 7 of “Marketing Analytics: A Modern Toolkit” (and optionally from the Appendix: “SQL”)

Tutorial

• Tutorial

Coaching session

• Work on your team goals for this course week (see coaching #3 on your workplan).

After the lecture

• After-class exercises (to be shared on Canvas)
• Optional (for students that need extra guidance in developing R skills):
  • DataCamp Introduction to Tidyverse (chapter 1 and 3)
  • DataCamp Cleaning Data in R (chapter 1 and 2)
  • DataCamp Joining Data with dplyr (chapter 1 and 2)